The present invention relates generally to the field of signature analysis, and more particularly to a micromechanical sensor for real-time, time-frequency analysis of signals.
Future electro-mechanical machines and structures will increasingly participate in their own service and maintenance using embedded distributed self-diagnostics that are remotely accessible to monitor machine health, detect and isolate subtle performance degradation, and in some cases even reconfigure some machines to adapt to changing operating environments. Traditionally, corrective maintenance and preventative maintenance have been the only two service paradigms. An estimate for the cost of service and maintenance for one major equipment manufacturer, however, is on the order of tens of billions of dollars. More recently, predictive or condition-based maintenance, enabled by low-cost sensors is emerging as an alternative.
Condition-based maintenance is just-in-time maintenance based on the actual health of the machine and its components. Since it avoids the cumulative cost of unnecessary service calls associated with preventative maintenance and the occurrence of machine failure and degradation associated with corrective maintenance, condition-based maintenance provides substantial cost savings.
Real-time signal analysis is critical for a variety of applications including condition-based monitoring and damage assessment for structures and electro-mechanical systems. Fault manifestation in machine vibration signals, however, is typically non-stationary in that the frequencies describing the faults vary over time. Identifying signatures of these types of faults requires analysis of properties of signals, such as frequency content, that vary over time. To isolate and identify a fault in a motor bearing, for example, the onset and temporal pattern of the changes in the spectral content of the signal must be determined. Traditional Fourier methods, including the short time Fourier transform (STFT), allows analysis of the time-varying properties of a signal that are important for diagnosis purposes.
Micro-Electro-Mechanical Systems (MEMS) integrate mechanical elements, such as microsensors and microactuators, and electronics on a common substrate through the utilization of microfabrication technology. MEMS are typically micromachined using integrated circuit (IC) compatible batch-processing techniques that selectively etch away parts of a silicon wafer or add new structural layers. They range in size from several micrometers to many millimeters. These systems sense, control, and actuate on a micro scale and function individually or in arrays to generate effects on a macro scale. MEMS sensors are known in the prior art and have been integrated into conventional non-MEMS signature analysis systems. The first problem with conventional systems is that sensors such as a tuning fork are not suitable for measuring time-varying spectral content of a signal because of the frequency-dependent damping constant of each individual fork. Second, the use of electronic processing in conventional systems make them susceptible to electronic interference. Third, the electronic processing requires adequate electrical power supply. Fourth, conventional systems are typically bulky in size because of the multitude of discrete component modules such as sensors, electronics, and readout. Thus, conventional systems are less portable than a monolithic MEMS implementation of the entire system.
In light of the foregoing, there is a need for a micromechanical sensor for signal detection an fault diagnosis that provides time-based windowing of event capture with control sequencing and data interpretation.
Accordingly, the present invention is directed to a MEMS system that allows real-time signal detection and fault diagnosis that substantially obviates one or more of the problems due to limitations and disadvantages of the related art.
In accordance with the purposes of the present invention, as embodied and broadly described, the invention provides a micro-electro-mechanical system for signature analysis including an array of sensors that measure a time varying event, wherein the array outputs a time-windowed sensor signal in response to operation of a system of interest. The sensor further includes a plurality of signal templates representing normal and faulty operating conditions of the system of interest, at least one logic unit that compares the sensor signal with the signal templates and provides a diagnostic state based on the comparison, and a readout device that outputs the diagnostic state as an external signal.
In another embodiment, the present invention provides a micro-electro-mechanical system for signature analysis of a system including an array of sensors detecting a physical phenomenon of interest, wherein the array of sensors has a time-frequency configuration that represents a template of a faulty operating condition of the system and a readout device that outputs an external signal indicating the faulty operating condition if the detected physical phenomenon matches the fault template.
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate several embodiments of the invention and together with the description serve to explain the principles of the invention.